Triple
T13767492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanan al-Shaykh |
E330787
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Hanan |
E44421
|
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: Hanan | Statement: [Hanan al-Shaykh, givenName, Hanan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanan Context triple: [Hanan al-Shaykh, givenName, Hanan]
-
A.
Hanan
chosen
Hanan is a given name most notably borne by Palestinian legislator, activist, and scholar Hanan Ashrawi.
-
B.
Nahila
Nahila is a character from the novel "Gate of the Sun," which portrays the Palestinian experience through interwoven personal and historical narratives.
-
C.
Juhayna
Juhayna is a town in Egypt’s Sohag Governorate, located in Upper Egypt along the Nile Valley.
-
D.
Zeina
Zeina is a feminine given name commonly used in Arabic-speaking and Middle Eastern cultures, often associated with beauty and grace.
-
E.
Laila
Laila is a feminine given name used in various cultures, often associated with meanings like "night" or "dark beauty."
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0227f2c48190983ccc9395e4e7a2 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b0724ab481908448d71a1bd02253 |
completed | May 3, 2026, 8:30 p.m. |
Created at: April 9, 2026, 10:10 p.m.