Triple
T25242535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Umar ibn al-Khattab family |
E632505
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
Zayd ibn Umar
Zayd ibn Umar was a son of the second Rashidun caliph, Umar ibn al-Khattab, and a member of his notable early Islamic family.
|
E1711826
|
NE FINISHED |
How this triple was built (2 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: Zayd ibn Umar | Statement: [Umar ibn al-Khattab family, hasMember, Zayd ibn Umar]
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: Zayd ibn Umar Triple: [Umar ibn al-Khattab family, hasMember, Zayd ibn Umar]
Generated description
Zayd ibn Umar was a son of the second Rashidun caliph, Umar ibn al-Khattab, and a member of his notable early Islamic family.
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_69e75a8ec5f88190b9eba06ae42b413a |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f47e009e6481908efffcca7ab7ffa8 |
completed | May 1, 2026, 10:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11271c5604819092a6491a9731933d |
completed | May 23, 2026, 4:03 a.m. |
| NEDg | Description generation | batch_6a114877a4508190a78b43976eac1f7a |
completed | May 23, 2026, 6:25 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1148d725148190ac86970517d88d8a |
completed | May 23, 2026, 6:27 a.m. |
Created at: April 21, 2026, 1:08 p.m.