Triple
T636039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel H. Weiss |
E16623
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Daniel |
E21909
|
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: Daniel | Statement: [Daniel H. Weiss, givenName, Daniel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Context triple: [Daniel H. Weiss, givenName, Daniel]
-
A.
Daniel
chosen
Daniel is a biblical book in the Old Testament that recounts the visions and experiences of the prophet Daniel, emphasizing themes of faithfulness and divine sovereignty.
-
B.
Adam
Adam is the first human in Abrahamic religious traditions, whose disobedience in Eden is believed to have introduced sin into the human condition.
-
C.
Johanus
Johanus is a given name, likely a variant or diminutive of Johan, used as a personal first name in some cultures.
-
D.
Andreas
Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
-
E.
Jeffrey
Jeffrey is a masculine given name of Germanic origin, commonly used in English-speaking countries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a4936be1c88190af56540324b57da7 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49ee667f08190a0332b8f6c569e1a |
completed | March 1, 2026, 8:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a57405d6f48190b55542d50d3a1f22 |
completed | March 2, 2026, 11:27 a.m. |
Created at: March 1, 2026, 7:35 p.m.