Triple

T3206183
Position Surface form Disambiguated ID Type / Status
Subject Saint-Michel–Notre-Dame E67166 entity
Predicate hasStationCode P1289 FINISHED
Object SMND
SMND is the station code for the central Paris RER railway station Saint-Michel–Notre-Dame, a major hub near Notre-Dame Cathedral.
 E335778 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: SMND | Statement: [Saint-Michel–Notre-Dame, hasStationCode, SMND]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SMND
Context triple: [Saint-Michel–Notre-Dame, hasStationCode, SMND]
  • A. SMR
    SMR is the three-letter ISO 3166-1 alpha-3 country code assigned to San Marino.
  • B. SMN2
    SMN2 is a human gene that produces a backup form of survival motor neuron protein and is a key therapeutic target in spinal muscular atrophy.
  • C. MS
    MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • D. MS
    MS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of Montserrat in the Caribbean.
  • E. MS
    MS is a postgraduate Master of Science degree typically focused on advanced study and research in scientific or technical disciplines.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: SMND
Triple: [Saint-Michel–Notre-Dame, hasStationCode, SMND]
Generated description
SMND is the station code for the central Paris RER railway station Saint-Michel–Notre-Dame, a major hub near Notre-Dame Cathedral.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SMND
Target entity description: SMND is the station code for the central Paris RER railway station Saint-Michel–Notre-Dame, a major hub near Notre-Dame Cathedral.
  • A. SMR
    SMR is the three-letter ISO 3166-1 alpha-3 country code assigned to San Marino.
  • B. SMN2
    SMN2 is a human gene that produces a backup form of survival motor neuron protein and is a key therapeutic target in spinal muscular atrophy.
  • C. MS
    MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • D. MS
    MS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of Montserrat in the Caribbean.
  • E. MS
    MS is a postgraduate Master of Science degree typically focused on advanced study and research in scientific or technical disciplines.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ad8589bd988190afa7ed2bdffb7b33 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaa56c21c8190b6aa7c56cb15ad56 completed March 8, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24bcf7b2481908bc52cfa71bd313c completed March 12, 2026, 5:14 a.m.
NEDg Description generation batch_69b24cb0c5f0819083ea589ded12ef3b completed March 12, 2026, 5:18 a.m.
NED2 Entity disambiguation (via description) batch_69b24d2ce888819087cc7c3f5db0e859 completed March 12, 2026, 5:20 a.m.
Created at: March 8, 2026, 3:07 p.m.